Velocity-weighted analysis of user equipment location data

ABSTRACT

Velocity-weighted analysis of UE location data is disclosed. UE velocity can be determined from the change in position and time. UE dwell can also be determined from the change in position and time. UE dwell and UE velocity can be inversely related. UE dwell an UE velocity can be correlated to a likelihood that an event occurrence related to a point of interest affects UE travel between a first and second location. A location of the point of interest can be determined to be in a region corresponding to a path between the first and second location. The region can be associated with the UE dwell and/or UE velocity, such that a probability of interaction can be determined for the event occurrence for the point of interest. The region can comprise a probable UE path based on historical UE data.

TECHNICAL FIELD

The disclosed subject matter relates to a velocity-weighted analysis ofuser equipment (UE) location data that enables correlation of a dwellvalue to an event for a geographic location.

BACKGROUND

UE location data can indicate a location of a UE at a point in time.Conventional UE location data analysis can determine a path of travelfor a UE, e.g., a route the UE takes in time and space. The path oftravel can be correlated to a geographical location associated with apoint of interest (POI). The movement of a UE relative to a POI can beemployed to evaluate the effect of an event associated with the POI. Asan example, in a conventional system, a POI can be grocery store and anevent can be an advertisement served to nearby UEs. UE location data canconventionally be employed to determine an effect of the advertising onUE traffic to the grocery store. Conventional systems, however, can do apoor job of providing analysis where the UE has a less than explicitresponse to the event, e.g., where the UE does not directly approach thegrocery store, etc. Moreover, conventional systems can fail to considerother events that occur in an area comprising the POI, e.g., the areacan comprise the grocery store, a gas station, a drug store, a fast foodrestaurant, etc., that can each be associated with events that canaffect UE paths near the grocery store. As an example, where the grocerystore is near a gas station and the grocery and the gas station bothhave an advertising event, a conventional system can falsely attributean uptick in grocery store traffic to the grocery advertisement and failto evaluate the effects of the gas station advertisement, moreespecially where the conventional system can be ignorant of other eventsin the area of the grocery store POI.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an illustration of an example system that can enabledetermining interaction probability data based on a velocity-weightedanalysis of user equipment location data, in accordance with aspects ofthe subject disclosure.

FIG. 2 is an illustration of an example system that can facilitatedetermining interaction probability data, based on a velocity-weightedanalysis of user equipment location data relative to a probable userequipment route, in accordance with aspects of the subject disclosure.

FIG. 3 is an illustration of an example system that can facilitatedetermining interaction probability data for communication to a remotelylocated component in accordance with aspects of the subject disclosure.

FIG. 4 is an illustration of an example system that can facilitateremotely determining interaction probability data in accordance withaspects of the subject disclosure.

FIG. 5 illustrates an example method enabling determination of aninteraction probability, based on a velocity-weighted analysis of userequipment location data, in accordance with aspects of the subjectdisclosure.

FIG. 6 illustrates an example method facilitating determining aninteraction probability based on a dwell value and a geographical area,derived from historical movement data, in accordance with aspects of thesubject disclosure.

FIG. 7 illustrates an example method facilitating determining aninteraction probability based on a dwell value and a location errorcorrected geographical area in accordance with aspects of the subjectdisclosure.

FIG. 8 illustrates an example method enabling determining a responsevalue based on a deviation of an interaction probability from a baselineinteraction probability in accordance with aspects of the subjectdisclosure.

FIG. 9 depicts an example schematic block diagram of a computingenvironment with which the disclosed subject matter can interact.

FIG. 10 illustrates an example block diagram of a computing systemoperable to execute the disclosed systems and methods in accordance withan embodiment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the subject disclosure. It may be evident, however,that the subject disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing the subjectdisclosure.

UE location data can be generated by a variety of sources, e.g., globalpositioning system (GPS), triangulation, multilateration, proximity toan access point (AP), timed fingerprint location (TFL), network eventlocating system (NELOS), etc. This location data can indicate a locationof a UE at a point in time. UE location data can, in some embodiments,be comprised in common location format (CLF) data. Generally, thelocation data can also be associated with a degree of accuracy. Theaccuracy can correspond to the location technology sourcing the UElocation data. UE location data can be used to determine a path oftravel for a UE. The path of travel can be correlated to a geographicallocation associated with a point of interest (POI). Where a probable UEroute comprises a portion of an area associated with a POI, the UElocation data can be analyzed to evaluate the effect of POI event(s) onUE movement. Changes in UE movement, e.g., UE velocity and/or UE dwell,etc., can be indicative of an effect of a POI event. As an example,while a conventional system can indicate that an event likely resultedin more UE traffic to a geographic location of a POI, the presentlydisclosed subject matter can indicate that the event resulted in the UElingering in an area comprising the POI for a longer period of time. Assuch, even where the UE may not explicitly enter a grocery store inresponse to a served advertisement, it can be shown by the presentlydisclosed subject matter that the served advertisement resulted in achange in behavior of the UE that can be attributed to the advertingevent. Moreover, the presently disclosed subject matter can consider theinteraction of a plurality of events ascribed to one or more POIs on theoverall velocity of the UE. This can enable attribution of an effect tocombinations of events, for example, advertising for potato chips canhave a first effect, advertising for dip can have a second effect, andadvertising for chips and advertising for dip near in time can have athird effect that can be different than the sum of the first and secondeffect.

In an embodiment, the disclosed subject matter can determine a velocityof a UE. The velocity can be related to a change in the position of theUE, e.g., difference between a first location and a second location,between a first and second time. A geographical area can be associatedwith the change in position of the UE. The determined velocity can thenbe associated with the geographical area. A location of a POI can bedetermined. Where the POI location is within the geographical area ofthe UE change in position, the velocity of the UE for that change inposition can reflect effects of events, e.g., advertising served to theUE, traffic, weather, date/time, a user schedule associated with the UE,police action, sporting events, etc.

In some embodiments, historical UE travel between the first and thesecond position can be employed to determine different probable routesbetween the first and the second positions. As an example, historically,UEs moving from a museum, e.g., a first location, to a subway station,e.g., a second location, can traverse a sidewalk for two blocks, e.g., afirst route, or cut through a park, e.g., a second route. The examplesecond route can be faster than the example first route, perhaps becauseof avoiding pedestrian crossings, etc. As such, the present disclosurecan associate movement of a UE with a geographical area cutting throughthe park between the museum and the subway station. As a result, POIsin/near the park can be considered affected by the velocity of the UE,while POIs along the sidewalk can be considered unaffected by thevelocity of the UE. Furthermore, the geographic area can be adapted toreflect a distribution of use of the different routes, for example,where 99% of historical UEs use the second route, the geographical areacan be more constrained to the route through the park, while incontrast, where 52% of historical UEs use the second route and 48% usethe first route, the geographical area can be broadened to include someor all of the first route in addition to the second route. This, ineffect, can enable attribution of an event effect to POIs along both thefirst and second routes.

Additionally, location accuracy can be employed to adapt thegeographical area associated with UE travel. Where location data is of afirst accuracy, the geographic area can reflect a first level ofconstraint relative to the first and second locations. In contrast,where the location data is of a second accuracy, the geographic area canreflect a second level of constraint relative to the first and secondlocations. As an example, where the first accuracy is on the order of afew meters, then the geographical area can include POIs within a fewmeters of a probably route between the first and second locations, whilewhere the accuracy is on the order of several hundred meters, thegeographical area can be expanded to cover POIs within hundreds ofmeters of the probably route. It will be noted that where location datais being captured for UEs at an increasingly finer grain, adapting thegeographic area associated with a probable route can be increasinglyeffective.

In an aspect, a UE velocity can be based on a change in position overtime, V=(p₂−p₁)/(t₂−t₁). As such, moving from a museum (p₁) to a subwaystation (p₂) faster, results in an increased velocity because thedenominator decreases while the numerator remains the same. The UEvelocity can therefore be considered to represent the average rate oftravel for the UE between the first and second locations. Of note, thehigher the velocity, typically the less likely a user of the UE willhave to interact with POIs, e.g. if a UE races past a coffee stand at 50miles per hour, they can be far less likely to have stopped and ordereda coffee than if the UE had moved past the coffee stand at 2 miles perhour. As such, it can typically be accepted that, in general, higher UEvelocities correspond to less efficacious POI events.

UE dwell values can represent an amount of time a UE occupies an areabetween the first and second locations. UE dwell can be written asD=(t₂−t₁)/(p₂−p₁). Moreover, dwell can be normalized across multiplelocations, n, e.g.

$D_{12} = {\frac{( {t_{2} - t_{1}} )/( {p_{2} - p_{1}} )}{\sum\limits_{i = 1}^{n}{( {t_{n} - t_{n - 1}} )/( {p_{n} - p_{n - 1}} )}}.}$Normalized dwell can represent an amount of time per unit distance, suchthat higher dwell can be correlated to the UE spending more timetraveling per unit distance between the first and second locations.Where dwell values are greater, the UE can be understood to have movedbetween the first and second locations more slowly, e.g. at a lowervelocity, and therefore can be understood to have a higher likelihood ofbeing affected by a POI event. UE velocity can reflect UE movement perunit time, while UE dwell can reflect UE time spent per unit distance.UE velocity and UE dwell can be inversely related, e.g. a highervelocity corresponds to a lower dwell, etc.

In an embodiment, the UE velocity and/or UE dwell can be employed indetermining the geographical area of a probable route between the firstand second locations. The geographical area can be of nearly any shapeor volume. As an example, the geographical area can be an ovalcomprising the first and second locations, e.g. 270, 272, 276, of FIG.2, etc. As another example, the geographical area can be a rectangulararea comprising the first and second locations, e.g. 274 of FIG. 2, etc.As further examples, the geographical area can be a polygon, cube,rotated oval, toroid, or nearly any other shape. Moreover, in someembodiments, the geographical area can reflect probably routes, such ascan be determined from historical UE movement data, see the descriptionof FIG. 2 for some examples.

In an embodiment, UE location data can be employed to determineinteraction probability data, e.g., a scoring or ranking of how likelyan event is to result in interaction based on the velocity/dwell of a UEwith a geographical area corresponding to a location of a POI. Theinteraction probability data can be determined on a first side of acommunications framework and then be communicated to a remote componenton a second side of the communications framework for dissemination torequesting entities, e.g., a more distributed analysis. In someembodiments, the interaction probability data can be determined on thesecond side of the communications framework, based on UE location datafrom the first side of the communications framework, e.g. a morecentralized analysis.

To the accomplishment of the foregoing and related ends, the disclosedsubject matter, then, comprises one or more of the features hereinaftermore fully described. The following description and the annexed drawingsset forth in detail certain illustrative aspects of the subject matter.However, these aspects are indicative of but a few of the various waysin which the principles of the subject matter can be employed. Otheraspects, advantages, and novel features of the disclosed subject matterwill become apparent from the following detailed description whenconsidered in conjunction with the provided drawings.

FIG. 1 is an illustration of a system 100, which can facilitatedetermining interaction probability data based on a velocity-weightedanalysis of user equipment (UE) location data, in accordance withaspects of the subject disclosure. System 100 can comprise probabilityof interaction component (PIC) 110. PIC 110 can enable determininginteraction probability data 190, based on UE location data 140.Interaction probability data 190 can, in some embodiments, be based onUE location data 140 and historical geographical data (HGD) 150. In anaspect, interaction probability data 190 can represent a probability ofa user of a UE reacting to an event, particularly an event associatedwith an entity at a point of interest (POI). As an example, interactionprobability data 190 can represent a likelihood that movement of a UEchanged as a result of an advertising event associated with a store,such as going to the store, moving towards the store, slowing downproximate to the store, moving towards a competing store, etc.

Interaction probability data 190 can be based on UE location data 140.UE location data 140 can comprise a location and a corresponding time.In some embodiments, UE location data can comprise other information,e.g., an accuracy metric, a UE identifier, etc. UE location data 140 canbe employed to determine a change in position in time, e.g., movement ofthe UE from a first position at a first time to a second position at asecond time, etc. PIC 110 can comprise velocity determination component(VDC) 120. VDC 120 can determine a UE velocity based on UE location data140. UE velocity can represent a change in position per unit time. In anembodiment a UE velocity can be based on, V=(p₂−p₁)/(t₂−t₁), where V isthe UE velocity, p₂ is a second position, e.g. location, of the UE at asecond time, t₂, and p₁ is a first position of the UE, at a first timet₁. Typically, p₂ and p₁ are different and t₂ and t₁ are different. Itwill be noted that the values comprising UE location data 140, e.g. p₂,p₁, t₂, t₁, etc., can be received from one or more sources, for example,(p₂, t₂) can be derived from GPS data, while (p₁, t₁) can be derivedfrom Wi-Fi location technology, NELOS data, TFL data, triangulation, CLFdata, etc.

PIC 110 can comprise dwell determination component (DDC) 130. DDC 130can determine a UE dwell based on UE location data 140. UE dwell canrepresent a change in time per unit distance. In an embodiment a UEdwell can be based on, D=1/V=(t₂−t₁)/(p₂−p₁), where D is UE dwell, V isthe UE velocity, p₂ is a second position of the UE at a second time, t₂,and p₁ is a first position of the UE, at a first time t₁. Typically, p₂and p₁ are different and t₂ and t₁ are different. As was previouslynoted, the values comprising UE location data 140 can be received fromone or more sources. In an aspect, the UE velocity and the UE dwell canbe inversely related. In an embodiment, the UE dwell can be normalizedagainst other determined UE dwell values, n, e.g.,

$D_{12} = {\frac{( {t_{2} - t_{1}} )/( {p_{2} - p_{1}} )}{\sum\limits_{i = 1}^{n}{( {t_{n} - t_{n - 1}} )/( {p_{n} - p_{n - 1}} )}}.}$

In an aspect, the UE dwell, or alternatively the normalized UE dwell,hereinafter UE dwell, can be associated with a geographical areacomprising a probable route of the UE for the corresponding period. Assuch, the UE dwell can represent a relative amount of time the UE spendsin each distance unit of the geographical area, e.g., the larger the UEdwell, the longer the UE is in the geographical area, which caninherently increase the likelihood of a user of the UE experiencing aPOI event, e.g., an increased probability of interaction between theuser of the UE and the entity at the POI. Similarly, the UE velocity canalso be associated with the geographical area and can represent adistance change per unit time, e.g., the lower the UE velocity, thelonger the UE takes to traverse the geographic area and inherently thegreater the chance of interaction between the user of the UE and theentity at the POI.

PIC 110 can determine interaction probability data 190 based on the UEdwell or UE velocity for events associated with POIs comprised in thedetermined geographical area. Where a POI is located in the geographicalarea, the UE dwell value, or the UE velocity value, can correspond to aprobability of interaction. Where the POI is not located in thegeographical area, it is not sufficiently likely to be along a probablyUE route and an interaction probability need not be determined. As anexample, a UE moving from an office in a building to a parking lot inthe basement of the building may typically pass by a first vendingmachine on the same floor as the office, e.g., in the elevator lobby,but may rarely pass by a second vending machine in the lobby where theelevator ride to the basement parking simply bypasses the lobbyaltogether. In this example, the geographical area can comprise ahorizontal area from the office to the elevator including the locationof the first vending machine, the vertical elevator shaft, and thehorizontal area from the elevator to the parking, but can exclude thelobby, and therefore exclude the location of the second vending machine.Accordingly, in this example, an increase in the UE dwell value canindicate that a correlated event can increase the interactionprobability of a user of the UE with the first vending machine but notthe second vending machine.

In some embodiments, HGD 150 can facilitate determining the geographicalarea. HGD 150 can comprise historical UE route information. Thishistorical UE route information can be employed to determine probableroutes between a first and a second location and therefore be used todetermine a geographical area that can be associated with UE dwell or UEvelocity. In an aspect, HGD 150 can comprise date/time data, UEroute(s), concurrent event(s), user schedule information, etc. As such,HGD 150 can determine, for example, that a route form an office to aparking area typically uses a first bank of elevators before 7 pm, buttypically uses a second bank of elevators after 7 pm. As anotherexample, HGD 150 can be employed to determine a geographical area islarger during heavy weather and/or in heavy traffic to account for useof alternate roadways on a drive home from work being included inprobable routes between the office and home for a user of a UE.

In an aspect, the geographical area typically includes a first andsecond location associated with the UE dwell or UE velocity. Moreover,the geographical area can be of any shape or volume, e.g., circular,ovular, toroidal, square, rectangular, polygonal, or combinationsthereof. As an example, the geographical area in the office to parkingarea example above can be a three-dimensional shape comprising a volumeof the office floor, the elevator lobby, the elevator shaft for one ormore elevator banks, and a volume in the underground parking area.

FIG. 2 is an illustration of a system 200, which can facilitatedetermining interaction probability data, based on a velocity-weightedanalysis of UE location data relative to a probable user equipmentroute, in accordance with aspects of the subject disclosure. System 200can comprise home 201 at location 260. A UE can travel to car 203 atlocation 262. The movement of the UE can be employed to determine a UEdwell for geographical area 270 that comprises location 260 and location262. UE location data at 260 can be based, for example, on UEconnectivity to a home network, e.g., via Wi-Fi, femtocell, Bluetooth,etc. UE location data at 262, can be based, for example, on UEtriangulation via cell towers 202. The UE can travel, via car 203, fromlocation 262 to location 264. The movement of the UE can be employed todetermine a UE dwell for geographical area 272 that comprises location262 and location 264. UE location data at 264, can be based, forexample, on UE connectivity to access point (AP) 204. The UE can travelfrom location 264 to store 205 at location 266. The movement of the UEcan again be employed to determine a UE dwell for geographical area 274that comprises location 264 and location 266. UE location data at 266,can be based, for example, on UE GPS data. The UE can then travel fromlocation 266 to location 268. Again, the movement of the UE can beemployed to determine a UE dwell for geographical area 276 thatcomprises location 266 and location 268. UE location data at 268, can bebased, for example, on inertial movement location technology.

The shape of geographical areas 270, 272, 274, and 276 can be the same,not illustrated, or different. Intuitively, walking from home 201 atlocation 260 to car 203 located at 262 can take a variety or routes,although typically those routes are likely to be less circuitous thanperhaps a casual stroll for exercise. As such, the example geographicalarea 270 can include areas around location 260 and 262 as well as somealternate routes between 260 and 262, leading to the width of theelliptical shape. Comparing geographical area 270 to geographical area272, area 272 can be narrower that area 270 owing to car 203 beingconstrained to driving on the road between 262 and 264. Between 264 and266, car 203 can have multiple alternative routes on a city block gridsystem, which can result in the rectangular shape of geographical area274. Further, the drive to 268 from 266 can include several alternativeroutes that can go, for example, past 268 before return thereto,contributing to an extension of the region of geographical area 276beyond location 268. Moreover, an error associated with the inertialmovement location technology used to determine the UE location atlocation 268 can contribute to altering the shape of area 276 and resultin some of the bulbous nature of area 276 around location 268.

FIG. 3 is an illustration of a system 300, which can facilitatedetermining interaction probability data for communication to a remotelylocated component, in accordance with aspects of the subject disclosure.System 300 can comprise PIC 310. PIC 310 can comprise VDC 320 that candetermine UE velocity data based on UE location data 340. PIC 310 cancomprise DDC 330 that can determine UE dwell data based on UE locationdata 340. PIC 310 can correlate UE dwell data or UE velocity data to ageographical area corresponding to a probable UE route. The probable UEroute can be determined based on UE location data 340. In someembodiments, the probable UE route can be further based on HGD 350.

In an aspect, PIC 310 can be located on a first side of a communicationframework that is located remotely from remote component(s) 382 andremote data store(s) 384. As an example, PIC 310 can be located in aradio access network (RAN) component, a NodeB or eNodeB device, afemtocell, picocell, access point, a UE, a wearable device, a laptopcomputer, a tablet computer, etc., and can communicate interactionprobability data to remote component(s) 382, for example a server,virtual device, a cloud device, a carrier-side component, etc., viacommunication framework 380, e.g., a network, the internet, etc. System300 enables distributed determination of interaction probability datathat can then be communicated to more central component, which can storeand distribute the interaction probability data. As an example, remotecomponent(s) 382 can be a virtualized network operator component thatserves interaction probability data to advertising entities. In someembodiments, the interaction probability data can be further processedon the remote side of communication framework 380, e.g., via remotecomponent(s) 382, for example, to determine a response value correlatingan event at a POI to the interaction probability data, e.g., a UE dwellvalue, UE velocity value, etc., to gauge the efficacy of the event.

FIG. 4 is an illustration of a system 400, which can facilitate remotelydetermining interaction probability data in accordance with aspects ofthe subject disclosure. System 400 can comprise PIC 410. PIC 410 cancomprise VDC 420 that can determine UE velocity data based on UElocation data 440. PIC 410 can comprise DDC 430 that can determine UEdwell data based on UE location data 440. PIC 410 can correlate UE dwelldata or UE velocity data to a geographical area corresponding to aprobable UE route. The probable UE route can be determined based on UElocation data 440. In some embodiments, the probable UE route can befurther based on HGD 450.

In an aspect, UE data 440 and/or HGD 450 can be received from a firstside of communication framework. Moreover, PIC 410 can be located on asecond side of a communication framework that is located remotely fromsources of UE data 440 and/or HGD 450. Remote component(s) 482 andremote data store(s) 484 can be located on the second side ofcommunication framework 480. As an example, PIC 410 can be located in aserver, virtual device, a cloud device, a carrier-side component, etc.PIC 410 can receive UE location data 440 and/or HGD 450 viacommunication framework 480, e.g., a network, the internet, etc., from aradio access network (RAN) component, a NodeB or eNodeB device, afemtocell, picocell, access point, a UE, a wearable device, a laptopcomputer, a tablet computer, etc. PIC 410 can communicate interactionprobability data to remote component(s) 482. System 400 enablescentralized determination of interaction probability data that can thenbe stored, e.g., via remote data store(s) 484, and distributed. As anexample, PIC 410 can be located on a network operator core-networkdevice and communicate interaction probability data remote component(s)482 that can be a server operated by, for example, a third party entity.

In view of the example system(s) described above, example method(s) thatcan be implemented in accordance with the disclosed subject matter canbe better appreciated with reference to flowcharts in FIG. 5-FIG. 8. Forpurposes of simplicity of explanation, example methods disclosed hereinare presented and described as a series of acts; however, it is to beunderstood and appreciated that the claimed subject matter is notlimited by the order of acts, as some acts may occur in different ordersand/or concurrently with other acts from that shown and describedherein. For example, one or more example methods disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methods. Furthermore,not all illustrated acts may be required to implement a describedexample method in accordance with the subject specification. Furtheryet, two or more of the disclosed example methods can be implemented incombination with each other, to accomplish one or more aspects hereindescribed. It should be further appreciated that the example methodsdisclosed throughout the subject specification are capable of beingstored on an article of manufacture (e.g., a computer-readable medium)to allow transporting and transferring such methods to computers forexecution, and thus implementation, by a processor or for storage in amemory.

FIG. 5 illustrates example method 500 facilitating determination of aninteraction probability, based on a velocity-weighted analysis of userequipment location data, in accordance with aspects of the subjectdisclosure. Method 500, at 510, can comprise receiving second UElocation data that is different from first UE location data. UE locationdata can comprise a location, a corresponding time, accuracy metric,etc. As such, at 520, method 500 can determine a temporal delta value,e.g., a difference in time values, based on the first and secondlocation data. At 530, method 500 can comprise determining a locationdelta value, e.g., a difference in location values, based on the firstand second location data.

At 540, method 500 can determine a UE velocity based on the temporaldelta value and the location delta value. The UE velocity can bedetermined from V=(p₂−p₁)/(t₂−t₁), where V is the UE velocity, (p₂−p₁)is the location delta value and (t₂−t₁) is the temporal delta value,with p₂ being the UE location at the second time, t₂, and p₁ being theUE location at the first time t₁. Typically, p₂ and p₁ are different andt₂ and t₁ are different. It will be noted that the values comprising thesecond UE location data, e.g. p₂, t₂, and the values comprising thefirst UE location data, e.g. p₁, t₁, can be received from one or moresources, for example, (p₂, t₂) can be derived from GPS data, while (p₁,t₁) can be derived from Wi-Fi location technology, NELOS data, TFL data,triangulation, CLF data, etc.

At 550, a geographic area can be determined based on the first andsecond location data. In an aspect, the geographic area can be any shapeor volume and typically comprises the first and second locationassociated with the first and second location data at 510. In an aspect,the geographical area can represent an area comprising a route a UE cantake between the first and second location. As an example, a defaultrectangular geographical area can be selected with a corner on each ofthe first and second locations, e.g. see area 274 of FIG. 2,representing that a route between 264 and 266 lie within are 274.Moreover, more advanced determinations the geographical area can beperformed and can comprise adapting the geographical area based onerror/accuracy of the UE location data, historical UE route data, mapdata, roadway data, etc.

At 560, method 500 can comprise determining an interaction probabilitybased on the UE velocity and the geographical area. At this point,method 500 can end. The interaction probability can represent aprobability that a user of a UE will respond to an event attributed to apoint of interest (POI). Where the POI location is within thegeographical area from 550, the UE velocity correspond to theprobability of interaction. Where the UE velocity is high it can be adecrease in a likelihood that an interaction will occur in response tothe event, and where the UE velocity is low there can be an increasedlikelihood of interaction.

FIG. 6 illustrates example method 600 enabling determining aninteraction probability based on a dwell value and a geographical area,derived from historical movement data, in accordance with aspects of thesubject disclosure. Method 600, at 610, can comprise receiving second UElocation data that is different from first UE location data. UE locationdata can comprise a location, a corresponding time, accuracy metric,etc. As such, at 620, method 600 can determine a temporal delta value,e.g., a difference in time values, based on the first and secondlocation data. At 630, method 600 can comprise determining a locationdelta value, e.g., a difference in location values, based on the firstand second location data.

At 640, method 600 can determine a UE dwell value based on the temporaldelta value and the location delta value. The UE velocity can bedetermined from D=1/V=(t₂−t₁)/(p₂−p₁), where D is UE dwell, V is the UEvelocity, (p₂−p1 is the location delta value and t2−t1 is the temporaldelta value, with p2 being the UE location at the second time, t₂, andp₁ being the UE location at the first time t₁. Typically, p₂ and p₁ aredifferent and t₂ and t₁ are different. It will be noted that the valuescomprising the second UE location data, e.g. p₂, t₂, and the valuescomprising the first UE location data, e.g. p₁, t₁, can be received fromone or more sources.

At 650, a geographic area can be determined based on the first locationdata, second location data, and historical UE movement data. In anaspect, the geographic area can be any shape or volume and typicallycomprises the first and second location associated with the first andsecond location data at 610. In an aspect, the geographical area canrepresent an area comprising a route a UE can take between the first andsecond location. Moreover, the geographical area can comprise a probableroute of the UE for the corresponding period. The historical UE movementdata can be employed to determine probable routes between the first andthe second location and therefore be used to determine a geographicalarea. In an aspect, historical UE movement data can comprise date/timedata, UE route(s), concurrent event(s), user schedule information, etc.As such, historical UE movement data can be used to determine ageographical area that corresponded to a level of probability that aroute will be taken based on various historical factors. As an example,where historical UEs overwhelmingly use three of five possible routes ondays where a sporting event occurs, the geographical area can compriseand area encompassing the three routes when a sporting event isoccurring and can encompass the five routes when no sporting event isoccurring. Moreover, more advanced determinations the geographical areacan be performed and can comprise adapting the geographical area basedon error/accuracy of the UE location data, historical UE route data, mapdata, roadway data, etc.

At 660, method 600 can comprise determining an interaction probabilitybased on the UE dwell and the geographical area. At this point, method600 can end. The interaction probability can represent a probabilitythat a user of a UE will respond to an event attributed to a point ofinterest (POI). Where the POI location is within the geographical areafrom 650, the UE dwell corresponds to the probability of interaction.Where the UE dwell is low there can be a decrease in a likelihood thatan interaction will occur in response to the event, and where the UEdwell is high there can be an increased likelihood of interaction.

FIG. 7 illustrates example method 700 that facilitates determining aninteraction probability based on a dwell value and a location errorcorrected geographical area in accordance with aspects of the subjectdisclosure. Method 700, at 710, can comprise determining a UE dwellvalue based on time-correlated location data. The time-correlatedlocation data can comprise first location data at a first time andsecond location data at a second time. The first location data and thesecond location data can be different.

At 720, method 700 comprises determining a geographic area based on thefirst location data, the second location data, first error associatedwith the first location data, second error associated with the secondlocation data, and historical movement data. Historical UE movement datacan comprise date/time data, UE route(s), concurrent event(s), userschedule information, etc. As such, historical UE movement data can beused to determine a geographical area that corresponded to a level ofprobability that a route will be taken based on various historicalfactors. Moreover, more advanced determinations the geographical areacan be performed and can comprise adapting the geographical area basedon accuracy of the UE location data, historical UE route data, map data,roadway data, etc. As an example, the geographical area can be adaptedbased on the error in the location data, e.g. increasing location errorcan correspond to an increase in the geographical area to moreconfidently include the location in the geographical area.

At 730, an interaction probability can be determined by method 700. Theinteraction probability can be based in the UE dwell value, thegeographical area, and a location of a POI. At this point method 700 canend. The interaction probability can represent a probability that a userof a UE will respond to an event attributed to a point of interest(POI). Where the POI location is within the geographical area from 720,the UE dwell can correspond to the probability of interaction. Where theUE dwell is high it can increase a likelihood that an interaction willoccur in response to the event, and where the UE dwell is low there canbe decreased likelihood of interaction.

FIG. 8 illustrates example method 800 facilitating determination of aresponse value based on a deviation of an interaction probability from abaseline interaction probability in accordance with aspects of thesubject disclosure. Method 800, at 810, can comprise determining anormalized UE dwell value based on time-correlated location data. Thetime-correlated location data can comprise first location data at afirst time and second location data at a second time. The first locationdata and the second location data can be different. The normalized UEdwell value between location 1 and location 2, e.g., the first andsecond locations, normalized to n total locations can be represented by

${D_{12} = \frac{( {t_{2} - t_{1}} )/( {p_{2} - p_{1}} )}{\sum\limits_{i = 1}^{n}{( {t_{n} - t_{n - 1}} )/( {p_{n} - p_{n - 1}} )}}},$where D₁₂ is the UE dwell between 1 and 2, p₂ is a second position ofthe UE at a second time, t₂, and p₁ is a first position of the UE, at afirst time t₁. Typically, p₂ and p₁ are different and t₂ and t₁ aredifferent.

At 820, method 800 comprises determining a geographic area based on thetime-correlated location data, e.g., the first location data and thesecond location data, and historical movement data. Historical UEmovement data can comprise date/time data, UE route(s), concurrentevent(s), user schedule information, etc. As such, historical UEmovement data can be used to determine a geographical area thatcorresponded to a level of probability that a route will be taken basedon various historical factors. Moreover, more advanced determinationsthe geographical area can be performed and can comprise adapting thegeographical area based on error/accuracy of the UE location data,historical UE route data, map data, roadway data, etc.

At 830, an interaction probability can be determined by method 800. Theinteraction probability can be based in the normalized UE dwell value,the geographical area, and a location of a POI. The interactionprobability can represent a probability that a user of a UE will respondto an event attributed to a point of interest (POI). Where the POIlocation is within the geographical area from 820, the UE dwell cancorrespond to the probability of interaction. Where the UE dwell is highit can increase a likelihood that an interaction will occur in responseto the event, and where the UE dwell is low there can be decreasedlikelihood of interaction.

At 840, method 800, can determine a response value. The response valuecan be based on a difference between the interaction probability and abaseline interaction probability. At this point method 800 can end. Thebaseline interaction probability can represent interaction probabilitiesrelated to historical UE movement, particularly with regard to thehistorical UE movement between the locations of the time-correlatedlocation data. In an aspect, this can be viewed as monitoring thehistorical likelihood of interaction for UEs traversing between twopoints and then, based on a change from the baseline to the instantinteraction probability, as correlated to an event occurrence,determining a response value. The response value can then be correlatedto the event occurrence. In an example, a baseline interaction responsecan be determined for UEs passing by a gas station on a lonely stretchof highway. Where the gas station adds a fast food kiosk, the UE dwelland UE velocities for a geographical area including the gas station canchange, resulting in a change in the interaction probability. Thischange in the interaction probability can be attributed to the additionof the fast food kiosk and the effect can be measured by the responsevalue.

FIG. 9 is a schematic block diagram of a computing environment 900 withwhich the disclosed subject matter can interact. The system 900comprises one or more remote component(s) 910. The remote component(s)910 can be hardware and/or software (e.g., threads, processes, computingdevices). In some embodiments, remote component(s) 910 can compriseservers, personal servers, wireless telecommunication core-networkdevices, etc. As an example, remote component(s) 910 can be a server orvirtual component, e.g., as disclosed in relation to FIG. 3, that canreceive interaction probability data, e.g., 190, from localcomponent(s), etc.

The system 900 also comprises one or more local component(s) 920. Thelocal component(s) 920 can be hardware and/or software (e.g., threads,processes, computing devices). In some embodiments, local component(s)920 can comprise femtocell(s), picocell(s), access point(s), RANdevice(s), NodeB(s), eNodeB(s), UE(s), personal computing device(s),wearable device(s), etc., for example, PIC 110, 310, 410, etc., can becomprised in an eNodeB, and can communicate, via a communicationframework, e.g., 380, 480, etc., to a remote device(s), e.g., 382, 482,etc.

One possible communication between a remote component(s) 910 and a localcomponent(s) 920 can be in the form of a data packet adapted to betransmitted between two or more computer processes. Another possiblecommunication between a remote component(s) 910 and a local component(s)920 can be in the form of circuit-switched data adapted to betransmitted between two or more computer processes in radio time slots.The system 900 comprises a communication framework 940 that can beemployed to facilitate communications between the remote component(s)910 and the local component(s) 920, and can comprise an air interface,e.g., Uu interface of a UMTS network, via a long-term evolution (LTE)network, etc. Remote component(s) 910 can be operably connected to oneor more remote data store(s) 950, such as a hard drive, solid statedrive, SIM card, device memory, etc., that can be employed to storeinformation on the remote component(s) 910 side of communicationframework 940. Similarly, local component(s) 920 can be operablyconnected to one or more local data store(s) 930, that can be employedto store information on the local component(s) 920 side of communicationframework 940. As examples, interaction probability data 190, etc., canbe stored on remote data store(s) 384, 484, 950 of a remote component910, UE location data 140, 340, 440, etc., HGD 150, 350, 450, etc., canbe stored on local data store(s) 930, etc., or remote data store(s) 384,484, 950.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 10, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that performs particulartasks and/or implement particular abstract data types.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It is noted that thememory components described herein can be either volatile memory ornonvolatile memory, or can comprise both volatile and nonvolatilememory, by way of illustration, and not limitation, volatile memory 1020(see below), non-volatile memory 1022 (see below), disk storage 1024(see below), and memory storage 1046 (see below). Further, nonvolatilememory can be included in read only memory, programmable read onlymemory, electrically programmable read only memory, electricallyerasable read only memory, or flash memory. Volatile memory can compriserandom access memory, which acts as external cache memory. By way ofillustration and not limitation, random access memory is available inmany forms such as synchronous random access memory, dynamic randomaccess memory, synchronous dynamic random access memory, double datarate synchronous dynamic random access memory, enhanced synchronousdynamic random access memory, Synchlink dynamic random access memory,and direct Rambus random access memory. Additionally, the disclosedmemory components of systems or methods herein are intended to comprise,without being limited to comprising, these and any other suitable typesof memory.

Moreover, it is noted that the disclosed subject matter can be practicedwith other computer system configurations, comprising single-processoror multiprocessor computer systems, mini-computing devices, mainframecomputers, as well as personal computers, hand-held computing devices(e.g., personal digital assistant, phone, watch, tablet computers,netbook computers, . . . ), microprocessor-based or programmableconsumer or industrial electronics, and the like. The illustratedaspects can also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network; however, some if not all aspects ofthe subject disclosure can be practiced on stand-alone computers. In adistributed computing environment, program modules can be located inboth local and remote memory storage devices.

FIG. 10 illustrates a block diagram of a computing system 1000 operableto execute the disclosed systems and methods in accordance with anembodiment. Computer 1012, which can be, for example, PIC 110, 310, 410,etc., can comprise a processing unit 1014, a system memory 1016, and asystem bus 1018. System bus 1018 couples system components comprising,but not limited to, system memory 1016 to processing unit 1014.Processing unit 1014 can be any of various available processors. Dualmicroprocessors and other multiprocessor architectures also can beemployed as processing unit 1014.

System bus 1018 can be any of several types of bus structure(s)comprising a memory bus or a memory controller, a peripheral bus or anexternal bus, and/or a local bus using any variety of available busarchitectures comprising, but not limited to, industrial standardarchitecture, micro-channel architecture, extended industrial standardarchitecture, intelligent drive electronics, video electronics standardsassociation local bus, peripheral component interconnect, card bus,universal serial bus, advanced graphics port, personal computer memorycard international association bus, Firewire (Institute of Electricaland Electronics Engineers 1194), and small computer systems interface.

System memory 1016 can comprise volatile memory 1020 and nonvolatilememory 1022. A basic input/output system, containing routines totransfer information between elements within computer 1012, such asduring start-up, can be stored in nonvolatile memory 1022. By way ofillustration, and not limitation, nonvolatile memory 1022 can compriseread only memory, programmable read only memory, electricallyprogrammable read only memory, electrically erasable read only memory,or flash memory. Volatile memory 1020 comprises read only memory, whichacts as external cache memory. By way of illustration and notlimitation, read only memory is available in many forms such assynchronous random access memory, dynamic read only memory, synchronousdynamic read only memory, double data rate synchronous dynamic read onlymemory, enhanced synchronous dynamic read only memory, Synchlink dynamicread only memory, Rambus direct read only memory, direct Rambus dynamicread only memory, and Rambus dynamic read only memory.

Computer 1012 can also comprise removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample, disk storage 1024. Disk storage 1024 comprises, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, flash memory card, or memory stick. In addition, disk storage1024 can comprise storage media separately or in combination with otherstorage media comprising, but not limited to, an optical disk drive suchas a compact disk read only memory device, compact disk recordabledrive, compact disk rewritable drive or a digital versatile disk readonly memory. To facilitate connection of the disk storage devices 1024to system bus 1018, a removable or non-removable interface is typicallyused, such as interface 1026.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media cancomprise, but are not limited to, read only memory, programmable readonly memory, electrically programmable read only memory, electricallyerasable read only memory, flash memory or other memory technology,compact disk read only memory, digital versatile disk or other opticaldisk storage, magnetic cassettes, magnetic tape, magnetic disk storageor other magnetic storage devices, or other tangible media which can beused to store desired information. In this regard, the term “tangible”herein as may be applied to storage, memory or computer-readable media,is to be understood to exclude only propagating intangible signals perse as a modifier and does not relinquish coverage of all standardstorage, memory or computer-readable media that are not only propagatingintangible signals per se. In an aspect, tangible media can comprisenon-transitory media wherein the term “non-transitory” herein as may beapplied to storage, memory or computer-readable media, is to beunderstood to exclude only propagating transitory signals per se as amodifier and does not relinquish coverage of all standard storage,memory or computer-readable media that are not only propagatingtransitory signals per se. Computer-readable storage media can beaccessed by one or more local or remote computing devices, e.g., viaaccess requests, queries or other data retrieval protocols, for avariety of operations with respect to the information stored by themedium. As such, for example, a computer-readable medium can compriseexecutable instructions stored thereon that, in response to execution,cause a system comprising a processor to perform operations, comprisingdetermining interaction probability data 190, etc., based on UE locationdata 140, 340, 440, etc., and/or HGD 150, 350, 450, etc.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

It can be noted that FIG. 10 describes software that acts as anintermediary between users and computer resources described in suitableoperating environment 1000. Such software comprises an operating system1028. Operating system 1028, which can be stored on disk storage 1024,acts to control and allocate resources of computer system 1012. Systemapplications 1030 take advantage of the management of resources byoperating system 1028 through program modules 1032 and program data 1034stored either in system memory 1016 or on disk storage 1024. It is to benoted that the disclosed subject matter can be implemented with variousoperating systems or combinations of operating systems.

A user can enter commands or information into computer 1012 throughinput device(s) 1036. In some embodiments, a user interface can allowentry of user preference information, etc., and can be embodied in atouch sensitive display panel, a mouse/pointer input to a graphical userinterface (GUI), a command line controlled interface, etc., allowing auser to interact with computer 1012. Input devices 1036 comprise, butare not limited to, a pointing device such as a mouse, trackball,stylus, touch pad, keyboard, microphone, joystick, game pad, satellitedish, scanner, TV tuner card, digital camera, digital video camera, webcamera, cell phone, smartphone, tablet computer, etc. These and otherinput devices connect to processing unit 1014 through system bus 1018 byway of interface port(s) 1038. Interface port(s) 1038 comprise, forexample, a serial port, a parallel port, a game port, a universal serialbus, an infrared port, a Bluetooth port, an IP port, or a logical portassociated with a wireless service, etc. Output device(s) 1040 use someof the same type of ports as input device(s) 1036.

Thus, for example, a universal serial busport can be used to provideinput to computer 1012 and to output information from computer 1012 toan output device 1040. Output adapter 1042 is provided to illustratethat there are some output devices 1040 like monitors, speakers, andprinters, among other output devices 1040, which use special adapters.Output adapters 1042 comprise, by way of illustration and notlimitation, video and sound cards that provide means of connectionbetween output device 1040 and system bus 1018. It should be noted thatother devices and/or systems of devices provide both input and outputcapabilities such as remote computer(s) 1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1044. Remote computer(s) 1044 can be a personal computer, a server, arouter, a network PC, cloud storage, a cloud service, code executing ina cloud-computing environment, a workstation, a microprocessor basedappliance, a peer device, or other common network node and the like, andtypically comprises many or all of the elements described relative tocomputer 1012. A cloud computing environment, the cloud, or othersimilar terms can refer to computing that can share processing resourcesand data to one or more computer and/or other device(s) on an as neededbasis to enable access to a shared pool of configurable computingresources that can be provisioned and released readily. Cloud computingand storage solutions can store and/or process data in third-party datacenters which can leverage an economy of scale and can view accessingcomputing resources via a cloud service in a manner similar to asubscribing to an electric utility to access electrical energy, atelephone utility to access telephonic services, etc.

For purposes of brevity, only a memory storage device 1046 isillustrated with remote computer(s) 1044. Remote computer(s) 1044 islogically connected to computer 1012 through a network interface 1048and then physically connected by way of communication connection 1050.Network interface 1048 encompasses wire and/or wireless communicationnetworks such as local area networks and wide area networks. Local areanetwork technologies comprise fiber distributed data interface, copperdistributed data interface, Ethernet, Token Ring and the like. Wide areanetwork technologies comprise, but are not limited to, point-to-pointlinks, circuit-switching networks like integrated services digitalnetworks and variations thereon, packet switching networks, and digitalsubscriber lines. As noted below, wireless technologies may be used inaddition to or in place of the foregoing.

Communication connection(s) 1050 refer(s) to hardware/software employedto connect network interface 1048 to bus 1018. While communicationconnection 1050 is shown for illustrative clarity inside computer 1012,it can also be external to computer 1012. The hardware/software forconnection to network interface 1048 can comprise, for example, internaland external technologies such as modems, comprising regular telephonegrade modems, cable modems and digital subscriber line modems,integrated services digital network adapters, and Ethernet cards.

The above description of illustrated embodiments of the subjectdisclosure, comprising what is described in the Abstract, is notintended to be exhaustive or to limit the disclosed embodiments to theprecise forms disclosed. While specific embodiments and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such embodiments andexamples, as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding Figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit, a digital signalprocessor, a field programmable gate array, a programmable logiccontroller, a complex programmable logic device, a discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Processorscan exploit nano-scale architectures such as, but not limited to,molecular and quantum-dot based transistors, switches and gates, inorder to optimize space usage or enhance performance of user equipment.A processor may also be implemented as a combination of computingprocessing units.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “selector,” “interface,” and the like are intendedto refer to a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration and not limitation, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software or firmwareapplication executed by a processor, wherein the processor can beinternal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can comprise a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Further, the term “include” is intended to be employed as an open orinclusive term, rather than a closed or exclusive term. The term“include” can be substituted with the term “comprising” and is to betreated with similar scope, unless otherwise explicitly used otherwise.As an example, “a basket of fruit including an apple” is to be treatedwith the same breadth of scope as, “a basket of fruit comprising anapple.”

Moreover, terms like “user equipment (UE),” “mobile station,” “mobile,”subscriber station,” “subscriber equipment,” “access terminal,”“terminal,” “handset,” and similar terminology, refer to a wirelessdevice utilized by a subscriber or user of a wireless communicationservice to receive or convey data, control, voice, video, sound, gaming,or substantially any data-stream or signaling-stream. The foregoingterms are utilized interchangeably in the subject specification andrelated drawings. Likewise, the terms “access point,” “base station,”“Node B,” “evolved Node B,” “eNodeB,” “home Node B,” “home accesspoint,” and the like, are utilized interchangeably in the subjectapplication, and refer to a wireless network component or appliance thatserves and receives data, control, voice, video, sound, gaming, orsubstantially any data-stream or signaling-stream to and from a set ofsubscriber stations or provider enabled devices. Data and signalingstreams can comprise packetized or frame-based flows. Data or signalinformation exchange can comprise technology, such as, multiple-inputand multiple-output (MIMO) radio(s), long-term evolution (LTE), LTEtime-division duplexing (TDD), global system for mobile communications(GSM), GSM EDGE Radio Access Network (GERAN), Wi Fi, WLAN, WiMax,CDMA2000, LTE new radio-access technology (LTE-NX), massive MIMOsystems, etc.

Additionally, the terms “core-network”, “core”, “core carrier network”,“carrier-side”, or similar terms can refer to components of atelecommunications network that typically provides some or all ofaggregation, authentication, call control and switching, charging,service invocation, or gateways. Aggregation can refer to the highestlevel of aggregation in a service provider network wherein the nextlevel in the hierarchy under the core nodes is the distribution networksand then the edge networks. UEs do not normally connect directly to thecore networks of a large service provider but can be routed to the coreby way of a switch or radio access network. Authentication can refer todeterminations regarding whether the user requesting a service from thetelecom network is authorized to do so within this network or not. Callcontrol and switching can refer determinations related to the futurecourse of a call stream across carrier equipment based on the callsignal processing. Charging can be related to the collation andprocessing of charging data generated by various network nodes. Twocommon types of charging mechanisms found in present day networks can beprepaid charging and postpaid charging. Service invocation can occurbased on some explicit action (e.g. call transfer) or implicitly (e.g.,call waiting). It is to be noted that service “execution” may or may notbe a core network functionality as third party network/nodes may takepart in actual service execution. A gateway can be present in the corenetwork to access other networks. Gateway functionality can be dependenton the type of the interface with another network.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,”“prosumer,” “agent,” and the like are employed interchangeablythroughout the subject specification, unless context warrants particulardistinction(s) among the terms. It should be appreciated that such termscan refer to human entities or automated components (e.g., supportedthrough artificial intelligence, as through a capacity to makeinferences based on complex mathematical formalisms), that can providesimulated vision, sound recognition and so forth.

Aspects, features, or advantages of the subject matter can be exploitedin substantially any, or any, wired, broadcast, wirelesstelecommunication, radio technology or network, or combinations thereof.Non-limiting examples of such technologies or networks comprisebroadcast technologies (e.g., sub-Hertz, extremely low frequency, verylow frequency, low frequency, medium frequency, high frequency, veryhigh frequency, ultra-high frequency, super-high frequency, terahertzbroadcasts, etc.); Ethernet; X.25; powerline-type networking, e.g.,Powerline audio video Ethernet, etc.; femtocell technology; Wi-Fi;worldwide interoperability for microwave access; enhanced general packetradio service; third generation partnership project, long termevolution; third generation partnership project universal mobiletelecommunications system; third generation partnership project 2, ultramobile broadband; high speed packet access; high speed downlink packetaccess; high speed uplink packet access; enhanced data rates for globalsystem for mobile communication evolution radio access network;universal mobile telecommunications system terrestrial radio accessnetwork; or long term evolution advanced.

The term “infer” or “inference” can generally refer to the process ofreasoning about, or inferring states of, the system, environment, user,and/or intent from a set of observations as captured via events and/ordata. Captured data and events can include user data, device data,environment data, data from sensors, sensor data, application data,implicit data, explicit data, etc. Inference, for example, can beemployed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events. Inference can also refer to techniquesemployed for composing higher-level events from a set of events and/ordata. Such inference results in the construction of new events oractions from a set of observed events and/or stored event data, whetherthe events, in some instances, can be correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources. Various classification schemes and/or systems(e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, and data fusion engines) can beemployed in connection with performing automatic and/or inferred actionin connection with the disclosed subject matter.

What has been described above includes examples of systems and methodsillustrative of the disclosed subject matter. It is, of course, notpossible to describe every combination of components or methods herein.One of ordinary skill in the art may recognize that many furthercombinations and permutations of the claimed subject matter arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: receivingfirst location data representative of a first location for a userequipment at a first time; receiving second location data representativeof a second location of the user equipment at a second time; based onthe first location data and the second location data, determining avelocity value associated with travel of the user equipment between thefirst location and second location, wherein the velocity value is afunction of a distance between the first location and the secondlocation and a time difference between the first time and the secondtime; associating the velocity value with a first region comprising thefirst location and the second location; associating a dwell value withthe first region, and determining a probability of interaction based onthe velocity value, the first region, and a location of a point ofinterest, wherein the probability of interaction is a metric related toa likelihood that the travel of the user equipment changed as a resultof an event occurrence correlated to the point of interest.
 2. Thesystem of claim 1, wherein an increase in the probability of interactioncorresponds to a decrease in the velocity value.
 3. The system of claim1, the determining the probability of interaction is based on the dwellvalue, the first region, and the location of the point of interest. 4.The system of claim 3, wherein the dwell value is a second function ofthe distance between the first location and the second location and thetime difference between the first time and the second time, and whereinthe first function and the second function are different.
 5. The systemof claim 3, wherein the dwell value is the inverse of the velocityvalue.
 6. The system of claim 3, wherein an increase in the probabilityof interaction corresponds to an increase in the dwell value.
 7. Thesystem of claim 3, wherein the dwell value is a normalized dwell valuethat is a second function of the distance between the first location andthe second location, a time difference between the first time and thesecond time, and an averaged dwell value for n previous UE movements. 8.The system of claim 1, wherein the first region is different from asecond region based on the first location, the second location, a firstaccuracy level of the first location, and a second accuracy level of thesecond location, and wherein the second region is substituted for thefirst region in the determining the probability of interaction.
 9. Thesystem of claim 8, wherein the first accuracy level corresponds to adetermined type of location technology employed in determining the firstlocation.
 10. The system of claim 8, wherein the first accuracy level isnot a same level as the second accuracy level.
 11. The system of claim1, wherein the first region is based on historical user equipment travelbetween the first location and second location.
 12. The system of claim11, wherein the first region comprises a probable route of travel forthe user equipment between the first location and the second location.13. A method, comprising: receiving, by a system comprising a processorand in response to movement of a user equipment, second location datathat is different from first location data, wherein the first locationdata comprises a first position of the user equipment and acorresponding first time value, and wherein the second location datacomprises a second position of the user equipment and a correspondingsecond time value; determining, by the system, a dwell value as afunction of the first location data and the second location data;determining, by the system, a geographic region corresponding to a routebetween the first position of the user equipment from the first locationdata and the second position of the user equipment from the secondlocation data; associating, by the system, the dwell value with thegeographic region; in response to receiving, by the system, anindication of an occurrence of an event associated with a point ofinterest, determining that the point of interest is located in thegeographic region; and determining, by the system, a value representinga likelihood that the movement of the user equipment is responsive tothe occurrence of the event associated with the point of interest. 14.The method of claim 13, wherein the dwell value is a normalized dwellvalue and is a function the first location data, the second locationdata, and third location data comprising a third position of the userequipment and a corresponding third time value.
 15. The method of claim13, wherein the geographic region corresponds to a probable routebetween the first position and the second position based on historicaluser equipment travel data.
 16. The method of claim 13, wherein thegeographic region is adapted to correct for measurement errorcorresponding to a type of location determination technology employed indetermining the first position of the user equipment.
 17. The method ofclaim 13, wherein the second location data is received from a differentsource than the first location data.
 18. A non-transitorymachine-readable storage medium, comprising executable instructionsthat, when executed by a processor, facilitate performance ofoperations, comprising: receiving first location data for a userequipment, wherein the first location data comprises a first position ofthe user equipment at a first time; in response to identifying movementof the user equipment, receiving second location data for the userequipment, wherein the second location data comprises a second positionof the user equipment at a second time; determining a dwell value as afunction of the first position, the second position, the first time, andthe second time; associating the dwell value with a geographic regionencompassing a path between the first position and the second position,wherein the geographic region is based on historical user equipmentmovement data; determining that the point of interest is located inlocated within the geographic region; determining a likelihood thatmovement of the user equipment from the first position to the secondposition is affected by an occurrence of an event associated with thepoint of interest; and enabling access to a ranking of the event,wherein the ranking is based on the likelihood that the movement wasaffected by the occurrence.
 19. The non-transitory machine-readablestorage medium of claim 18, wherein the first location data is receivedfrom a first source, wherein the second location data is received from asecond source, and wherein the first source and the second source arenot a same source.
 20. The non-transitory machine-readable storagemedium of claim 18, wherein the first location data is received from afirst source, wherein the second location data is received from a secondsource, and wherein the first source and the second source capture aposition of the user equipment with different location determinationtechnologies.